Many industrial processes involve the manufacture of particles and the properties of the item of manufacture (pharmaceuticals, paint, food, chemicals, etc) depend heavily on the size of the particles used. Oftentimes these processes involve mixtures of particles, such as a mixture of particles of one material having more than one particle size or a mixture of particles of more than one material wherein the mixture includes more than one particle size. However, current particle sizing techniques do not generally distinguish between the different sizes of particles in a given particle size distribution and report a “universal” particle size distribution for all particles present in a sample. If a feature is observed, for example a peak due to fine particles, it is not known which constituent of a mixture contributes to that particular particle size.
Some current techniques for measuring particle size distributions for small particles, such as Dynamic Light Scattering, rely upon the Brownian motion of the particles to derive estimates of their size measurements. However, computations of size estimates can take several minutes and these techniques are therefore not ideal for online processes. Further, when the particles are not monodisperse (i.e., all of substantially one size), signals measured from movements of the larger particles can tend to blur signals measured from movements of the smaller particles to the extent that the smaller particles are not properly measured, or not even detected at all.
U.S. Pat. No. 5,121,629 discloses a method for measuring particle size distribution and concentration based on directing ultrasonic waves through a suspension of particles in a suspending medium. Size distribution and concentration calculations include fitting two lognormal distributions to the measurements, based on the assumption that the particle size distribution is the sum of two lognormal distributions. There is no basis for this assumption and it sometimes leads to incorrect solutions. The Powell Discriminator described can erroneously lead to a local minimum solution that is not the overall global minimum solution and is therefore the wrong solution. Also, these calculations are not fast, taking on the order of thirty minutes to calculate particle size distribution and concentration values for a single measurement.
U.S. Pat. No. 7,257,518 discloses a method of calculating particle size distributions and concentrations of particles that are densely concentrated, so that multiple scattering effects must be accounted for. This method relies upon nonlinear methods of estimating the particle size distribution and concentrations and can take a considerable amount of time to calculate measurement estimates.
Some existing particle size distribution estimation methods provide plots of estimates that are unacceptably noisy (e.g., spiky). There is a need for improved methods for providing estimations that are smoothed and therefore provide more definite values for a distribution and values that can be more readily read and ascertained by a user.
There is a continuing need for fast and accurate methods of measuring size distribution of small particles, particularly for use in online applications, for real time or near-real time calculations of measurements during performance of a process where small particles are employed. Even for offline applications, it would be desirable to provide faster, accurate methods of measuring particle size distributions.